The objective function of optimization models usually includes multiple components to achieve an optimal balance among multiple conflicting objectives such as to decrease cost versus to increase service level. Users often need to adjust the weights of objective components to meet different goals. The selection of weight settings helps in achieving the optimal and practical solution for a given set of business goals, but is a challenging task for a user, e.g., with little operation research background, given the highly abstract mathematical expressions.
The standard practice in the industry and academia is to set weights to an optimization function or model based on user experiences, intuition or through testing of different weight settings to determine the preferred weights. The selection of weight settings helps in achieving the optimal and practical solution for a given set of business goals, but is a challenging task for a user. The mathematical formulas built by operation research specialists may not be intelligible enough for users to set weights accurately purely based on their experiences or intuition, which may lead to sub-optimal impractical solutions. Further, testing of different weight settings can get time-consuming especially for a large scale problem.
The present disclosure describes a self-learning approach to automatically generate weight settings, for example, optimal weight settings, for a given set of goals.